Deep learning is the machine learning technique behind the most exciting capabilities in diverse areas like robotics, natural language processing, image recognition and artificial intelligence (including the famous AlphaGo). In this course, you'll gain hands-on, practical knowledge of how to use deep learning with Keras 2.0, the latest version of a cutting edge library for deep learning in Python.
In this chapter, you'll become familiar with the fundamental concepts and terminology used in deep learning, and understand why deep learning techniques are so powerful today. You'll build simple neural networks yourself and generate predictions with them.
Here, you'll learn how to optimize the predictions generated by your neural networks. You'll do this using a method called backward propagation, which is one of the most important techniques in deep learning. Understanding how it works will give you a strong foundation to build from in the second half of the course.
In this chapter, you'll use the keras library to build deep learning models for both regression as well as classification! You'll learn about the Specify-Compile-Fit workflow that you can use to make predictions and by the end of this chapter, you'll have all the tools necessary to build deep neural networks!
Here, you'll learn how to optimize your deep learning models in keras. You'll learn how to validate your models, understand the concept of model capacity, and experiment with wider and deeper networks. Enjoy!